---
license: ecl-2.0
task_categories:
- text-classification
language:
- en
tags:
- public
- text
- education
- sentiment-analysis
pretty_name: TweetFeels 1m6
size_categories:
- 1M<n<10M
configs:
- config_name: dataset
  data_files:
  - split: train
    path: 
    - "tweets_1m6_processed.csv"
---

# TweetFeels 1m6

Collection of over a million English-language tweets harvested through the Twitter API in 2009. Each tweet carries a sentiment label automatically inferred from the presence of positive or negative emoticons: 0 for negative and 4 for positive (the original release also encodes 2 for neutral, though this class is sparsely populated).  

The dataset is distributed as a single CSV file with six tab-separated fields:  

- **target** – sentiment polarity (0, 2, 4)  
- **ids** – unique tweet ID  
- **date** – UTC timestamp of posting  
- **flag** – search query that matched the tweet, or “NO_QUERY”  
- **user** – screen name of the posting account  
- **text** – full tweet text, emoticons and URLs preserved  

No manual annotation was performed; labels were assigned via distant supervision based on emoticon presence. The corpus covers diverse topics and informal language patterns typical of Twitter, making it a standard benchmark for large-scale sentiment analysis and social-media text-mining tasks.

**Acknowledgements**: The dataset is available through [Kaggle—Sentiment140](https://www.kaggle.com/datasets/kazanova/sentiment140?resource=download).